BP网络在生物滴滤塔去除硫化氢中的仿真

摘要

It was difficult to develop a normal parametric model to match the results well for the complex non-linear relationship among the inlet concentration,retention time and removal efficiency of hydrogen sulfide in the biotrickling filter.Aiming at this problem,back-propagation neural network（BPNN） was introduced into the research.A non-parametric model was established by the training of BPNN with the experimental data.And this model could simulate and predict the experimental results well.The correlation coefficient of simulation with all experimental data was 0.986 0.In nine different experimental conditions,the linear regression correlation coefficient of network predicted values and the corresponding experimental results was as high as 0.965 9.%在生物滴滤塔对硫化氢的去除过程中,H2S的进气质量浓度、停留时间和去除率之间存在着复杂的非线性关系,使得常规的建模方法很难获得理想的结果.针对这一问题,引入BP神经网络,通过BP网络对试验数据的学习建立系统的非参数模型,并利用该模型对系统进行仿真与预测.结果表明,经过训练的BP网络模型可以很好地对系统进行仿真和预测,对全部试验数据网络仿真的相关系数为0.986 0;在9组不同的试验条件下,网络预测值与相应的试验结果值线性回归相关系数高达0.965 9.